






import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
							from PIL import Image
else:


							class 	__SCREAMING_SNAKE_CASE:
														@staticmethod
														def lowerCAmelCase_			(  *UpperCamelCase:   Any							,						**UpperCamelCase:   Optional[Any]    )		->		Union[str, Any]:
																		pass
def   a_			(   _A	)      ->      str:







				"""simple docstring"""
				snake_case__						=  hashlib.mda(image.tobytes()	)
				return m.hexdigest()[:10]
def   a_			(   _A	)      ->      Dict:







				"""simple docstring"""
				snake_case__						=  np.array(UpperCAmelCase_	)
				snake_case__						=  npimg.shape
				return {"hash": hashimage(UpperCAmelCase_	), "shape": shape}


@is_pipeline_test
@require_vision
@require_torch
class 	__SCREAMING_SNAKE_CASE(						unittest.TestCase						):
							_UpperCAmelCase			     =							dict(
							    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()						) if MODEL_FOR_MASK_GENERATION_MAPPING else [])						)
							_UpperCAmelCase			     =							dict(
							    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()						) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])						)
							def lowerCAmelCase_			(  self:   List[Any]							,						UpperCamelCase:   Optional[int]							,						UpperCamelCase:   Union[str, Any]							,						UpperCamelCase:   Dict    )		->		Dict:
											snake_case__						=  MaskGenerationPipeline(model=UpperCAmelCase_							,						image_processor=UpperCAmelCase_    )
											return image_segmenter, [
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											]
							def lowerCAmelCase_			(  self:   Optional[int]							,						UpperCamelCase:   int							,						UpperCamelCase:   Optional[int]    )		->		Tuple:
											pass
							@require_tf
							@unittest.skip('Image segmentation not implemented in TF'    )
							def lowerCAmelCase_			(  self:   Optional[Any]    )		->		Optional[int]:
											pass
							@slow
							@require_torch
							def lowerCAmelCase_			(  self:   Tuple    )		->		Any:
											snake_case__						=  pipeline('mask-generation'							,						model='facebook/sam-vit-huge'    )

											snake_case__						=  image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'							,						points_per_batch=2_56    )

											# Shortening by hashing
											snake_case__						=  []
											for i, o in enumerate(outputs['masks']    ):
															new_outupt += [{"mask": mask_to_test_readable(UpperCAmelCase_    ), "scores": outputs["scores"][i]}]

											# fmt: off
											self.assertEqual(
											    nested_simplify(UpperCAmelCase_							,						decimals=4    )							,						[
											        {'mask': {'hash': '115ad19f5f', 'shape': (4_80, 6_40)}, 'scores': 1.0_444},
											        {'mask': {'hash': '6affa964c6', 'shape': (4_80, 6_40)}, 'scores': 1.021},
											        {'mask': {'hash': 'dfe28a0388', 'shape': (4_80, 6_40)}, 'scores': 1.0_167},
											        {'mask': {'hash': 'c0a5f4a318', 'shape': (4_80, 6_40)}, 'scores': 1.0_132},
											        {'mask': {'hash': 'fe8065c197', 'shape': (4_80, 6_40)}, 'scores': 1.0_053},
											        {'mask': {'hash': 'e2d0b7a0b7', 'shape': (4_80, 6_40)}, 'scores': 0.9_967},
											        {'mask': {'hash': '453c7844bd', 'shape': (4_80, 6_40)}, 'scores': 0.993},
											        {'mask': {'hash': '3d44f2926d', 'shape': (4_80, 6_40)}, 'scores': 0.9_909},
											        {'mask': {'hash': '64033ddc3f', 'shape': (4_80, 6_40)}, 'scores': 0.9_879},
											        {'mask': {'hash': '801064ff79', 'shape': (4_80, 6_40)}, 'scores': 0.9_834},
											        {'mask': {'hash': '6172f276ef', 'shape': (4_80, 6_40)}, 'scores': 0.9_716},
											        {'mask': {'hash': 'b49e60e084', 'shape': (4_80, 6_40)}, 'scores': 0.9_612},
											        {'mask': {'hash': 'a811e775fd', 'shape': (4_80, 6_40)}, 'scores': 0.9_599},
											        {'mask': {'hash': 'a6a8ebcf4b', 'shape': (4_80, 6_40)}, 'scores': 0.9_552},
											        {'mask': {'hash': '9d8257e080', 'shape': (4_80, 6_40)}, 'scores': 0.9_532},
											        {'mask': {'hash': '32de6454a8', 'shape': (4_80, 6_40)}, 'scores': 0.9_516},
											        {'mask': {'hash': 'af3d4af2c8', 'shape': (4_80, 6_40)}, 'scores': 0.9_499},
											        {'mask': {'hash': '3c6db475fb', 'shape': (4_80, 6_40)}, 'scores': 0.9_483},
											        {'mask': {'hash': 'c290813fb9', 'shape': (4_80, 6_40)}, 'scores': 0.9_464},
											        {'mask': {'hash': 'b6f0b8f606', 'shape': (4_80, 6_40)}, 'scores': 0.943},
											        {'mask': {'hash': '92ce16bfdf', 'shape': (4_80, 6_40)}, 'scores': 0.943},
											        {'mask': {'hash': 'c749b25868', 'shape': (4_80, 6_40)}, 'scores': 0.9_408},
											        {'mask': {'hash': 'efb6cab859', 'shape': (4_80, 6_40)}, 'scores': 0.9_335},
											        {'mask': {'hash': '1ff2eafb30', 'shape': (4_80, 6_40)}, 'scores': 0.9_326},
											        {'mask': {'hash': '788b798e24', 'shape': (4_80, 6_40)}, 'scores': 0.9_262},
											        {'mask': {'hash': 'abea804f0e', 'shape': (4_80, 6_40)}, 'scores': 0.8_999},
											        {'mask': {'hash': '7b9e8ddb73', 'shape': (4_80, 6_40)}, 'scores': 0.8_986},
											        {'mask': {'hash': 'cd24047c8a', 'shape': (4_80, 6_40)}, 'scores': 0.8_984},
											        {'mask': {'hash': '6943e6bcbd', 'shape': (4_80, 6_40)}, 'scores': 0.8_873},
											        {'mask': {'hash': 'b5f47c9191', 'shape': (4_80, 6_40)}, 'scores': 0.8_871}
											    ]							,						)
											# fmt: on


							@require_torch
							@slow
							def lowerCAmelCase_			(  self:   Union[str, Any]    )		->		List[Any]:
											snake_case__						=  'facebook/sam-vit-huge'
											snake_case__						=  pipeline('mask-generation'							,						model=UpperCAmelCase_    )

											snake_case__						=  image_segmenter(
											    'http://images.cocodataset.org/val2017/000000039769.jpg'							,						pred_iou_thresh=1							,						points_per_batch=2_56    )

											# Shortening by hashing
											snake_case__						=  []
											for i, o in enumerate(outputs['masks']    ):
															new_outupt += [{"mask": mask_to_test_readable(UpperCAmelCase_    ), "scores": outputs["scores"][i]}]

											self.assertEqual(
											    nested_simplify(UpperCAmelCase_							,						decimals=4    )							,						[
											        {'mask': {'hash': '115ad19f5f', 'shape': (4_80, 6_40)}, 'scores': 1.0_444},
											        {'mask': {'hash': '6affa964c6', 'shape': (4_80, 6_40)}, 'scores': 1.0_210},
											        {'mask': {'hash': 'dfe28a0388', 'shape': (4_80, 6_40)}, 'scores': 1.0_167},
											        {'mask': {'hash': 'c0a5f4a318', 'shape': (4_80, 6_40)}, 'scores': 1.0_132},
											        {'mask': {'hash': 'fe8065c197', 'shape': (4_80, 6_40)}, 'scores': 1.0_053},
											    ]							,						)






"""simple docstring"""


import numpy as np



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_ = 1E-12		,   UpperCAmelCase_ = 1_00		,   )->				tuple[float, np.ndarray]:







       """simple docstring"""
       assert np.shape(UpperCAmelCase_					)[0] == np.shape(UpperCAmelCase_					)[1]
       # Ensure proper dimensionality.
       assert np.shape(UpperCAmelCase_					)[0] == np.shape(UpperCAmelCase_					)[0]
       # Ensure inputs are either both complex or both real
       assert np.iscomplexobj(UpperCAmelCase_					) == np.iscomplexobj(UpperCAmelCase_					)
       UpperCamelCase       			=		np.iscomplexobj(UpperCAmelCase_					)
       if is_complex:
              # Ensure complex input_matrix is Hermitian
              assert np.array_equal(UpperCAmelCase_		,   input_matrix.conj().T					)

       # Set convergence to False. Will define convergence when we exceed max_iterations
       # or when we have small changes from one iteration to next.

       UpperCamelCase       			=		False
       UpperCamelCase       			=		0
       UpperCamelCase       			=		0
       UpperCamelCase       			=		1E12

       while not convergence:
              # Multiple matrix by the vector.
              UpperCamelCase       			=		np.dot(UpperCAmelCase_		,   UpperCAmelCase_					)
              # Normalize the resulting output vector.
              UpperCamelCase       			=		w / np.linalg.norm(UpperCAmelCase_					)
              # Find rayleigh quotient
              # (faster than usual b/c we know vector is normalized already)
              UpperCamelCase       			=		vector.conj().T if is_complex else vector.T
              UpperCamelCase       			=		np.dot(UpperCAmelCase_		,   np.dot(UpperCAmelCase_		,   UpperCAmelCase_					)					)

              # Check convergence.
              UpperCamelCase       			=		np.abs(lambda_ - lambda_previous					) / lambda_
              iterations += 1

              if error <= error_tol or iterations >= max_iterations:
                     UpperCamelCase       			=		True

              UpperCamelCase       			=		lambda_

       if is_complex:
              UpperCamelCase       			=		np.real(lambda_					)

       return lambda_, vector



def 						lowerCamelCase__      (				)->				None:







       """simple docstring"""
       UpperCamelCase       			=		np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]					)
       UpperCamelCase       			=		np.array([41, 4, 20]					)
       UpperCamelCase       			=		real_input_matrix.astype(np.complexaaa					)
       UpperCamelCase       			=		np.triu(1j * complex_input_matrix		,   1					)
       complex_input_matrix += imag_matrix
       complex_input_matrix += -1 * imag_matrix.T
       UpperCamelCase       			=		np.array([41, 4, 20]					).astype(np.complexaaa					)

       for problem_type in ["real", "complex"]:
              if problem_type == "real":
                     UpperCamelCase       			=		real_input_matrix
                     UpperCamelCase       			=		real_vector
              elif problem_type == "complex":
                     UpperCamelCase       			=		complex_input_matrix
                     UpperCamelCase       			=		complex_vector

              # Our implementation.
              UpperCamelCase			,							UpperCamelCase       			=		power_iteration(UpperCAmelCase_		,   UpperCAmelCase_					)

              # Numpy implementation.

              # Get eigenvalues and eigenvectors using built-in numpy
              # eigh (eigh used for symmetric or hermetian matrices).
              UpperCamelCase			,							UpperCamelCase       			=		np.linalg.eigh(UpperCAmelCase_					)
              # Last eigenvalue is the maximum one.
              UpperCamelCase       			=		eigen_values[-1]
              # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
              UpperCamelCase       			=		eigen_vectors[:, -1]

              # Check our implementation and numpy gives close answers.
              assert np.abs(eigen_value - eigen_value_max					) <= 1E-6
              # Take absolute values element wise of each eigenvector.
              # as they are only unique to a minus sign.
              assert np.linalg.norm(np.abs(UpperCAmelCase_					) - np.abs(UpperCAmelCase_					)					) <= 1E-6


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      test_power_iteration()





